{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "from scipy.stats import pearsonr\n",
    "from adjustText import adjust_text\n",
    "from scipy import stats\n",
    "from mpl_toolkits.axes_grid1.inset_locator import mark_inset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "pop_df=pd.read_csv(r'C:\\Users\\Yasaman\\Downloads\\World_bank_population.csv',skiprows=3)\n",
    "pop_df=pop_df[['Country Code','2003','2019']].dropna()\n",
    "pop_df['2019']=pop_df['2019'].astype(int)\n",
    "possible_countries=pop_df.query(\" `2019` >=1000000\")['Country Code'].values\n",
    "possible_countries=[x.lower() for x in possible_countries]\n",
    "\n",
    "\n",
    "\n",
    "excluded_iso3_codes = [\n",
    "    \"IRL\",  # Ireland\n",
    "    \"SSD\",  # South Sudan\n",
    "    \"SDN\",  # Sudan\n",
    "    \"COG\",  # Republic of the Congo\n",
    "    \"COD\",  # Democratic Republic of the Congo\n",
    "    \"GIN\",  # Guinea\n",
    "    \"GNB\",  # Guinea-Bissau\n",
    "    \"GNQ\",  # Equatorial Guinea\n",
    "    \"PNG\",  # Papua New Guinea\n",
    "    \"XKX\",  # Kosovo (unofficial)\n",
    "    \"MNE\",  # Montenegro\n",
    "    \"SRB\",  # Serbia\n",
    "    \"TLS\"   # Timor-Leste\n",
    "]\n",
    "excluded_iso3_codes=[c.lower() for c in excluded_iso3_codes]\n",
    "\n",
    "possible_iso=list(set(possible_countries)-set(excluded_iso3_codes))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(r\"C:\\Users\\Yasaman\\Downloads\\Attention-fractional counting.csv\")\n",
    "df=df[(df['affiliation_country'].isin(possible_iso))&df['country'].isin(possible_iso)]\n",
    "df=df.rename(columns={'year':'Year', 'aggregated_value':'count', 'country':'Mention_country', 'affiliation_country':'Aff_country'})\n",
    "Country_list={'Egypt':'EGY', 'Tunisia':'TUN','Libya':'LBY','Syria':'SYR','Yemen':'YEM','Bahrain':'BHR','Jordan':'JOR','Kuwait':'KWT','Morocco':'MAR','Oman':'OMN'}\n",
    "rev_Country_list={Country_list[key]: key for key in Country_list}\n",
    "abbr=[country.lower() for country in Country_list.values()]\n",
    "country_codes=pd.read_csv(r\"C:\\Users\\Yasaman\\Downloads\\iso3.csv\")\n",
    "country_codes['iso3']=[c.lower() for c in country_codes['iso3']]\n",
    "physical_sciences=[ 'MATH', 'ENGI', 'PHYS', 'COMP', \"MUL\"]\n",
    "df=df[~df['subjarea'].isin(physical_sciences)]\n",
    "map={country_codes.iloc[c]['iso3']: country_codes.iloc[c]['name'] for c in range(len(country_codes))}\n",
    "map['irn']='Iran'\n",
    "map['usa']='USA'\n",
    "map['gbr']='UK'\n",
    "filtered_df=df[(df['Mention_country'].isin(abbr))&(df['Mention_country']!=df['Aff_country'])]\n",
    "\n",
    "before_df=filtered_df[filtered_df['Year'].isin(np.arange(2002, 2011, 1))].groupby(by=['Mention_country','Aff_country']).sum().reset_index()[['count','Mention_country','Aff_country']].rename(columns={'count':'count_before'})\n",
    "after_df=filtered_df[filtered_df['Year'].isin(np.arange(2011, 2020, 1))].groupby(by=['Mention_country','Aff_country']).sum().reset_index()[['count','Mention_country','Aff_country']].rename(columns={'count':'count_after'})\n",
    "compare_df=before_df.merge(after_df, how='outer', on=['Mention_country','Aff_country']).fillna(0)\n",
    "compare_df['count_after']/=9\n",
    "compare_df['count_before']/=9\n",
    "compare_df=compare_df.groupby(['Aff_country'])[['count_before', 'count_after']].sum().reset_index()\n",
    "compare_df['difference']=compare_df['count_after']-compare_df['count_before']\n",
    "compare_df=compare_df.sort_values('difference', ascending=False)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "country_df=pd.read_csv(r\"C:\\Users\\Yasaman\\Downloads\\arabspring_number_of_papers_per_year_per_descipline_per_country.csv\")\n",
    "country_df=country_df[country_df['affiliation_country'].isin(possible_iso)]\n",
    "\n",
    "country_df=country_df.groupby(['affiliation_country'])['aggregated_value'].sum().reset_index()\n",
    "country_df=country_df.merge(compare_df, left_on='affiliation_country', right_on='Aff_country', how='left').dropna(subset=['Aff_country'])\n",
    "\n",
    "\n",
    "\n",
    "country_codes=pd.read_csv(r\"C:\\Users\\Yasaman\\Downloads\\iso3.csv\")\n",
    "country_codes['iso3']=[c.lower() for c in country_codes['iso3']]\n",
    "map={country_codes.iloc[c]['iso3']: country_codes.iloc[c]['name'] for c in range(len(country_codes))}\n",
    "map['irn']='Iran'\n",
    "map['usa']='USA'\n",
    "map['gbr']='UK'\n",
    "map['rus']='Russia'\n",
    "map['syr']='Syria'\n",
    "map['are']='UAE'\n",
    "plot_a_df=country_df.reset_index(drop=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "aus\n",
      "chn\n",
      "deu\n",
      "esp\n",
      "fra\n",
      "gbr\n",
      "ind\n",
      "irn\n",
      "ita\n",
      "jpn\n",
      "mys\n",
      "sau\n",
      "usa\n",
      "Pearson Correlation Coefficient: 0.6842136662770959\n",
      "P-value: 3.2930937649176193e-21\n",
      "Spearman Correlation Coefficient: 0.8451370468611848\n",
      "P-value: 1.8672973952285983e-40\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax1=plt.subplots(nrows=1, ncols=1)\n",
    "ins = ax1.inset_axes([0.08,0.61,0.45,0.35])\n",
    "\n",
    "\n",
    "sns.regplot(plot_a_df, x='aggregated_value', y='difference' ,marker=\"x\", color=\".2\", line_kws=dict(color=\"#7DE1DF\"),ax=ax1,  scatter_kws={\"zorder\":10}, robust=True)\n",
    "ax1.set_ylabel(r'$\\Delta A_i^*$', fontsize=18)\n",
    "ax1.set_xlabel('Total Scholarly Output', fontsize=18)\n",
    "\n",
    "\n",
    "sns.regplot(plot_a_df, x='aggregated_value', y='difference' ,marker=\"x\", color=\".2\", line_kws=dict(color=\"#7DE1DF\"),ax=ins,  scatter_kws={\"zorder\":10}, robust=True)\n",
    "ins.set_ylabel(r'', fontsize=10)\n",
    "ins.set_xlabel(r'', fontsize=10)\n",
    "ins.set_xlim(0, 2e5)\n",
    "ins.set_ylim(-15/8, 20)\n",
    "\n",
    "\n",
    "points=[]\n",
    "# Annotate each point\n",
    "for line in plot_a_df[(plot_a_df['difference']>30)|(plot_a_df['aggregated_value']>1e6)].index:\n",
    "\n",
    "    x = plot_a_df.aggregated_value[line]\n",
    "    y = plot_a_df.difference[line]\n",
    "    label =map[plot_a_df.affiliation_country[line]]\n",
    "    print(plot_a_df.affiliation_country[line])\n",
    "    points+=[ax1.text(x, y, label,\n",
    "                    fontsize=8, ha='center', va='center')]\n",
    "\n",
    "adjust_text(points, arrowprops=dict(arrowstyle=\"-\", color='k', lw=0.5, alpha=.5), expand=(1.5, 2.5))        \n",
    "\n",
    "points2=[]\n",
    "# Annotate each point\n",
    "for line in plot_a_df[(plot_a_df['difference']<=20)& (plot_a_df['difference']>8)&(plot_a_df['aggregated_value']<200000)].index:\n",
    "\n",
    "    x = plot_a_df.aggregated_value[line]\n",
    "    y = plot_a_df.difference[line]\n",
    "    label =map[plot_a_df.affiliation_country[line]]\n",
    "    points2+=[ins.text(x, y, label,\n",
    "                    fontsize=8, ha='center', va='center')]\n",
    "    \n",
    "adjust_text(points2, arrowprops=dict(arrowstyle=\"-\", color='k', lw=0.5, alpha=.5), expand=(1.5, 2), ax=ins)        \n",
    "\n",
    "\n",
    "\n",
    "\n",
    "correlation_coefficient, p_value = pearsonr(plot_a_df['difference'], plot_a_df['aggregated_value'])\n",
    "print(\"Pearson Correlation Coefficient:\", correlation_coefficient)\n",
    "print(\"P-value:\", p_value)\n",
    "\n",
    "correlation_coefficient, p_value = stats.spearmanr(plot_a_df['difference'], plot_a_df['aggregated_value'])\n",
    "print(\"Spearman Correlation Coefficient:\", correlation_coefficient)\n",
    "print(\"P-value:\", p_value)\n",
    "\n",
    "mark_inset(ax1, ins, loc1=2, loc2=4, fc=\"none\", ec=\"0.5\", linestyle=':')\n",
    "\n",
    "fig.savefig('attention_vs_scholarlyoutput.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>affiliation_country</th>\n",
       "      <th>aggregated_value</th>\n",
       "      <th>Aff_country</th>\n",
       "      <th>count_before</th>\n",
       "      <th>count_after</th>\n",
       "      <th>difference</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>usa</td>\n",
       "      <td>7.003973e+06</td>\n",
       "      <td>usa</td>\n",
       "      <td>176.459208</td>\n",
       "      <td>348.711034</td>\n",
       "      <td>172.251826</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    affiliation_country  aggregated_value Aff_country  count_before  \\\n",
       "136                 usa      7.003973e+06         usa    176.459208   \n",
       "\n",
       "     count_after  difference  \n",
       "136   348.711034  172.251826  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_a_df[plot_a_df['affiliation_country']=='usa']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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